CRCS: An automatic image processing pipeline for hormone level analysis of Cushing's disease.

Due to the abnormal secretion of adreno-cortico-tropic-hormone (ACTH) by tumors, Cushing's disease leads to hypercortisonemia, a precursor to a series of metabolic disorders and serious complications. Cushing's disease has high recurrence rate, short recurrence time and undiscovered recurrence reason after surgical resection. Qualitative or quantitative automatic image analysis of histology images can potentially in providing insights into Cushing's disease, but still no software has been available to the best of our knowledge. In this study, we propose a quantitative image analysis-based pipeline CRCS, which aims to explore the relationship between the expression level of ACTH in normal cell tissues adjacent to tumor cells and the postoperative prognosis of patients. CRCS mainly consists of image-level clustering, cluster-level multi-modal image registration, patch-level image classification and pixel-level image segmentation on the whole slide imaging (WSI). On both image registration and classification tasks, our method CRCS achieves state-of-the-art performance compared to recently published methods on our collected benchmark dataset. In addition, CRCS achieves an accuracy of 0.83 for postoperative prognosis of 12 cases. CRCS demonstrates great potential for instrumenting automatic diagnosis and treatment for Cushing's disease.

Perspective view of allogeneic IgG tumor immunotherapy.

Allogeneic tumors are eradicated by host immunity; however, it is unknown how it is initiated until the report in Nature by Yaron Carmi et al. in 2015. Currently, we know that allogeneic tumors are eradicated by allogeneic IgG via dendritic cells. AlloIgG combined with the dendritic cell stimuli tumor necrosis factor alpha and CD40L induced tumor eradication via the reported and our proposed potential signaling pathways. AlloIgG triggers systematic immune responses targeting multiple antigens, which is proposed to overcome current immunotherapy limitations. The promising perspectives of alloIgG immunotherapy would have advanced from mouse models to clinical trials; however, there are only 6 published articles thus far. Therefore, we hope this perspective view will provide an initiative to promote future discussion.

Oncolytic virotherapy evolved into the fourth generation as tumor immunotherapy.

BACKGROUND: Oncolytic virotherapy (OVT) is a promising anti-tumor modality that utilizes oncolytic viruses (OVs) to preferentially attack cancers rather than normal tissues. With the understanding particularly in the characteristics of viruses and tumor cells, numerous innovative OVs have been engineered to conquer cancers, such as Talimogene Laherparepvec (T-VEC) and tasadenoturev (DNX-2401). However, the therapeutic safety and efficacy must be further optimized and balanced to ensure the superior safe and efficient OVT in clinics, and reasonable combination therapy strategies are also important challenges worthy to be explored. MAIN BODY: Here we provided a critical review of the development history and status of OVT, emphasizing the mechanisms of enhancing both safety and efficacy. We propose that oncolytic virotherapy has evolved into the fourth generation as tumor immunotherapy. Particularly, to arouse T cells by designing OVs expressing bi-specific T cell activator (BiTA) is a promising strategy of killing two birds with one stone. Amazing combination of therapeutic strategies of OVs and immune cells confers immense potential for managing cancers. Moreover, the attractive preclinical OVT addressed recently, and the OVT in clinical trials were systematically reviewed. CONCLUSION: OVs, which are advancing into clinical trials, are being envisioned as the frontier clinical anti-tumor agents coming soon.

BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data.

Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

Artificial-cell-type aware cell-type classification in CITE-seq.

MOTIVATION: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. RESULTS: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. AVAILABILITY AND IMPLEMENTATION: http://github.com/QiuyuLian/CITE-sort. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Gut microbiota homeostasis restoration may become a novel therapy for breast cancer.

Breast cancer is the most diagnosed cancer in women. It significantly impairs a patient's physical and mental health. Gut microbiota comprise the bacteria residing in a host's gastrointestinal tract. Through studies over the last decade, we now know that alterations in the composition of the gut microbiome are associated with protection against colonization by pathogens and other diseases, such as diabetes and cancer. This review focuses on how gut microbiota can affect breast cancer development through estrogen activity and discusses the types of bacteria that may be involved in the onset and the progression of breast cancer. We also describe potential therapies to curtail the risk of breast cancer by restoring gut microbiota homeostasis and reducing systemic estrogen levels. This review will further explore the relationship between intestinal microbes and breast cancer and propose a method to treat breast cancer by improving intestinal microbes. We aimed at discovering new methods to prevent or treat BC by changing intestinal microorganisms.

GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.

BACKGROUND: Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments. RESULTS: We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm. CONCLUSION: GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.

GateKeeper: a new hardware architecture for accelerating pre-alignment in DNA short read mapping.

MOTIVATION: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -called short reads- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and 'candidate' locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms. RESULTS: We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing, on average, 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10. AVAILABILITY AND IMPLEMENTATION: https://github.com/BilkentCompGen/GateKeeper. CONTACT: mohammedalser@bilkent.edu.tr or onur.mutlu@inf.ethz.ch or calkan@cs.bilkent.edu.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimal seed solver: optimizing seed selection in read mapping.

MOTIVATION: Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the ability of the mapper in selecting less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds. RESULTS: We present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-base-pair read in [Formula: see text] operations on average and in [Formula: see text] operations in the worst case, while generating a maximum of [Formula: see text] seed frequency database lookups. We compare OSS against four state-of-the-art seed selection schemes and observe that OSS provides a 3-fold reduction in average seed frequency over the best previous seed selection optimizations. AVAILABILITY AND IMPLEMENTATION: We provide an implementation of the Optimal Seed Solver in C++ at: https://github.com/CMU-SAFARI/Optimal-Seed-Solver CONTACT: hxin@cmu.edu, calkan@cs.bilkent.edu.tr or onur@cmu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Shifted Hamming distance: a fast and accurate SIMD-friendly filter to accelerate alignment verification in read mapping.

MOTIVATION: Calculating the edit-distance (i.e. minimum number of insertions, deletions and substitutions) between short DNA sequences is the primary task performed by seed-and-extend based mappers, which compare billions of sequences. In practice, only sequence pairs with a small edit-distance provide useful scientific data. However, the majority of sequence pairs analyzed by seed-and-extend based mappers differ by significantly more errors than what is typically allowed. Such error-abundant sequence pairs needlessly waste resources and severely hinder the performance of read mappers. Therefore, it is crucial to develop a fast and accurate filter that can rapidly and efficiently detect error-abundant string pairs and remove them from consideration before more computationally expensive methods are used. RESULTS: We present a simple and efficient algorithm, Shifted Hamming Distance (SHD), which accelerates the alignment verification procedure in read mapping, by quickly filtering out error-abundant sequence pairs using bit-parallel and SIMD-parallel operations. SHD only filters string pairs that contain more errors than a user-defined threshold, making it fully comprehensive. It also maintains high accuracy with moderate error threshold (up to 5% of the string length) while achieving a 3-fold speedup over the best previous algorithm (Gene Myers's bit-vector algorithm). SHD is compatible with all mappers that perform sequence alignment for verification.

Fast and accurate mapping of Complete Genomics reads.

Many recent advances in genomics and the expectations of personalized medicine are made possible thanks to power of high throughput sequencing (HTS) in sequencing large collections of human genomes. There are tens of different sequencing technologies currently available, and each HTS platform have different strengths and biases. This diversity both makes it possible to use different technologies to correct for shortcomings; but also requires to develop different algorithms for each platform due to the differences in data types and error models. The first problem to tackle in analyzing HTS data for resequencing applications is the read mapping stage, where many tools have been developed for the most popular HTS methods, but publicly available and open source aligners are still lacking for the Complete Genomics (CG) platform. Unfortunately, Burrows-Wheeler based methods are not practical for CG data due to the gapped nature of the reads generated by this method. Here we provide a sensitive read mapper (sirFAST) for the CG technology based on the seed-and-extend paradigm that can quickly map CG reads to a reference genome. We evaluate the performance and accuracy of sirFAST using both simulated and publicly available real data sets, showing high precision and recall rates.

Multiplex PMA-qPCR Assay with Internal Amplification Control for Simultaneous Detection of Viable Legionella pneumophila, Salmonella typhimurium, and Staphylococcus aureus in Environmental Waters.

Pathogenic microorganisms are responsible for many infectious diseases, and pathogen monitoring is important and necessary for water quality control. This study for the first time explored a multiplex quantitative real-time PCR (qPCR) technique combined with propidium monoazide (PMA) to simultaneously detect viable Legionella pneumophila, Salmonella typhimurium, and Staphylococcus aureus in one reaction from water samples. Sodium lauroyl sarcosinate (sarkosyl) was applied to enhance the dead bacterial permeability of PMA. The sensitivity of the multiplex PMA-qPCR assay achieved two colony-forming units (CFU) per reaction for L. pneumophila and three CFU per reaction for S. typhimurium and S. aureus. No PCR products were amplified from all nontarget control samples. Significantly, with comparable specificity and sensitivity, this newly invented multiplex PMA-qPCR assay took a much shorter time than did conventional culture assays when testing water samples with spiked bacteria and simulated environmental water treatment. The viable multiplex PMA-qPCR assay was further successfully applied to pathogen detection from rivers, canals, and tap water samples after simple water pretreatment.

Anticancer bioactive peptide (ACBP) inhibits gastric cancer cells by upregulating growth arrest and DNA damage-inducible gene 45A (GADD45A).

Recently, we reported that anticancer bioactive peptide (ACBP), purified from goat spleens immunized with human gastric cancer extracts, significantly inhibited gastric cancer cells in vitro and gastric tumors in vivo via repressing cell growth and promoting apoptosis, making it a promising potential biological anticancer drug. However, it is not known what genes are functionally required for the ACBP effects. Here, we first found that two tumor suppressor genes, cyclin-dependent kinase inhibitor 2B (CDKN2B) and growth arrest and DNA damage-inducible alpha (GADD45A), were upregulated significantly in the cells with ACBP treatment by microarray screening and the findings were validated by real-time RT-PCR. Next, GADD45A mRNA and protein expressions were downregulated in the gastric cancer cells by lentivirus-mediated RNAi; then, cell viability, cell cycle, and apoptosis were assayed by MTT and flow cytometry. Interestingly, our results indicated that cell viability was not dependent on GADD45A without ACBP treatment; however, cell sensitivity to ACBP was significantly decreased in ACBP-treated gastric cancer cells with GADD45A downregulation. Therefore, we demonstrate that GADD45A was functionally required for ACBP to inhibit gastric cancer cells, suggesting that GADD45A may become a biomarker for ACBP sensitivity. Our findings have significant implications on the molecular mechanism understanding, biomarker development, and anticancer drug development of ACBP.

Gut microbiota plays a significant role in gout.

With the development of social economy, the incidence of gout is increasing, which is closely related to people's increasingly rich diet. Eating a diet high in purine, fat, sugar and low-fibre for a long time further aggravates gout by affecting uric acid metabolism. The renal metabolism mechanism of uric acid has been thoroughly studied. To find a new treatment method for gout, increasing studies have recently been conducted on the mechanism of intestinal excretion, metabolism and absorption of uric acid. The most important research is the relationship between intestinal microbiota and the risk of gout. Gut microbiota represent bacteria that reside in a host's gastrointestinal tract. The composition of the gut microbiota is associated with protection against pathogen colonization and disease occurrence. This review focuses on how gut microbiota affects gout through uric acid and discusses the types of bacteria that may be involved in the occurrence and progression of gout. We also describe potential therapy for gout by restoring gut microbiota homeostasis and reducing uric acid levels. We hold the perspective that changing intestinal microbiota may become a vital method for effectively preventing or treating gout.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma.

BACKGROUND: Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy. METHODS: We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP). RESULTS: Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region. CONCLUSIONS: These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Accelerating read mapping with FastHASH.

With the introduction of next-generation sequencing (NGS) technologies, we are facing an exponential increase in the amount of genomic sequence data. The success of all medical and genetic applications of next-generation sequencing critically depends on the existence of computational techniques that can process and analyze the enormous amount of sequence data quickly and accurately. Unfortunately, the current read mapping algorithms have difficulties in coping with the massive amounts of data generated by NGS.We propose a new algorithm, FastHASH, which drastically improves the performance of the seed-and-extend type hash table based read mapping algorithms, while maintaining the high sensitivity and comprehensiveness of such methods. FastHASH is a generic algorithm compatible with all seed-and-extend class read mapping algorithms. It introduces two main techniques, namely Adjacency Filtering, and Cheap K-mer Selection.We implemented FastHASH and merged it into the codebase of the popular read mapping program, mrFAST. Depending on the edit distance cutoffs, we observed up to 19-fold speedup while still maintaining 100% sensitivity and high comprehensiveness.

Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer.

BACKGROUND: For locally advanced rectal cancer (LARC), accurate response evaluation is necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and-wait (W&W) strategy. Algorithms based on deep learning have shown great value in medical image analyses. Here we used deep learning algorithms of endoscopic images for the assessment of NAT response in LARC. METHOD: 214 LARC patients were retrospectively included in the study. After NAT, these patients underwent total mesorectal excision (TME) surgery. Among them, 51 (23.8%) of the patients achieved a pathological complete response (pCR). 160 patients from Shanghai Changzheng Hospital were regarded as primary dataset, and the other 54 patients from Zhejiang Cancer Hospital were regarded as validation dataset. ResNet-18 and DenseNet-121 were applied to train the models based on endoscopic images after NAT. Deep learning models were valid in the validation dataset and compared to manual method. RESULTS: The performances were comparable in AUC between deep learning models and manual method. For mean metrics, sensitivity (0.750 vs. 0.417) and AUC (0.716 vs. 0.601) in ResNet-18 deep learning model were higher than those in the manual method. The deep learning models were able to identify the endoscopic features associated with NAT response by the heatmaps. A diagnostic flow diagram which integrated the deep learning model to assist the clinicians in making decisions for W&W strategy was constructed. CONCLUSIONS: We created deep learning models using endoscopic features for assessment of NAT in LARC. The deep learning models achieved modest accuracies and performed comparably to manual method.

The microbiota regulates hematopoietic stem and progenitor cell development by mediating inflammatory signals in the niche.

The commensal microbiota regulates the self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs) in bone marrow. Whether and how the microbiota influences HSPC development during embryogenesis is unclear. Using gnotobiotic zebrafish, we show that the microbiota is necessary for HSPC development and differentiation. Individual bacterial strains differentially affect HSPC formation, independent of their effects on myeloid cells. Early-life dysbiosis in chd8-/- zebrafish impairs HSPC development. Wild-type microbiota promote HSPC development by controlling basal inflammatory cytokine expression in kidney niche, and chd8-/- commensals elicit elevated inflammatory cytokines that reduce HSPCs and enhance myeloid differentiation. We identify an Aeromonas veronii strain with immuno-modulatory activities that fails to induce HSPC development in wild-type fish but selectively inhibits kidney cytokine expression and rebalances HSPC development in chd8-/- zebrafish. Our studies highlight the important roles of a balanced microbiome during early HSPC development that ensure proper establishment of lineal precursor for adult hematopoietic system.

Nanoparticles advanced from preclinical studies to clinical trials for lung cancer therapy.

Lung cancer is the leading cause of cancer mortality. As a heterogeneous disease, it has different subtypes and various treatment modalities. In addition to conventional surgery, radiotherapy and chemotherapy, targeted therapy and immunotherapy have also been applied in the clinics. However, drug resistance and systemic toxicity still cannot be avoided. Based on the unique properties of nanoparticles, it provides a new idea for lung cancer therapy, especially for targeted immunotherapy. When nanoparticles are used as carriers of drugs with special physical properties, the nanodrug delivery system ensures the accuracy of targeting and the stability of drugs while increasing the permeability and the aggregation of drugs in tumor tissues, showing good anti-tumor effects. This review introduces the properties of various nanoparticles including polymer nanoparticles, liposome nanoparticles, quantum dots, dendrimers, and gold nanoparticles and their applications in tumor tissues. In addition, the specific application of nanoparticle-based drug delivery for lung cancer therapy in preclinical studies and clinical trials is discussed.

Recent Advances on the Molecular Mechanism and Clinical Trials of Venous Thromboembolism.

Venous thromboembolism is a condition that includes deep vein thrombosis and pulmonary embolism. It is the third most common cardiovascular disease behind acute coronary heart disease and stroke. Over the past few years, growing research suggests that venous thrombosis is also related to the immune system and inflammatory factors have been confirmed to be involved in venous thrombosis. The role of inflammation and inflammation-related biomarkers in cerebrovascular thrombotic disease is the subject of ongoing debate. P-selectin leads to platelet-monocyte aggregation and stimulates vascular inflammation and thrombosis. The dysregulation of miRNAs has also been reported in venous thrombosis, suggesting the involvement of miRNAs in the progression of venous thrombosis. Plasminogen activator inhibitor-1 (PAI-1) is a crucial component of the plasminogen-plasmin system, and elevated levels of PAI-1 in conjunction with advanced age are significant risk factors for thrombosis. In addition, it has been showed that one of the ways that neutrophils promote venous thrombosis is the formation of neutrophil extracellular traps (NETs). In recent years, the role of extracellular vesicles (EVs) in the occurrence and development of VTE has been continuously revealed. With the advancement of research technology, the complex regulatory role of EVs on the coagulation process has been gradually discovered. However, our understanding of the causes and consequences of these changes in venous thrombosis is still limited. Therefore, we review our current understanding the molecular mechanisms of venous thrombosis and the related clinical trials, which is crucial for the future treatment of venous thrombosis.